Table 10.
The comparison of the accuracy rate (the best result) among the methods in diagnosing the BC based on the WBCD and WDBC database.
| WBCD | |
|---|---|
| Method | Accuracy |
| Linear SVM (71) | 96.72% |
| RF (66) | 96.10% |
| XGBoost (72) | 98.00% |
| XGBoost + RFE (56) | 99.02% |
| KNN (57) | 97.51% |
| C4.5 algorithm (69) | 96.70% |
| MLP & LR (61) | 98.00% |
| NB (59) | 97.36% |
| ANN (64) | 98.57% |
| Proposed method (FLN) | 99.51% |
| WDBC database | |
| Method | Accuracy |
| Weighted vote-based ensemble (58) | 95.09% |
| GA-classifier (55) | 96.60% |
| EM-PCA-CART-fuzzy rule-based (67) | 94.10% |
| LMNN-SRA (68) | 96.66% |
| SVM with linear kernel (70) | 98.24% |
| PCA + CNN (60) | 96.40% |
| Bayesian network (65) | 96.31% |
| Fuzzy-ID3+FUZZTDBD (63) | 94.53% |
| HCRF (62) | 97.05% |
| Proposed method (FLN) | 98.83% |